SambaFlow learning map

Welcome! This doc page is a learning map for users new to SambaNova. It helps you see the big picture and find the information you need quickly. Here’s an overview:

Tutorials: GitHub and doc

Many of us learn best by doing. This set of tutorials includes sample code on GitHub and code discussion in this doc set.

Find tutorial code and a README with instructions in our sambanova/tutorials public GitHub repo.
For each tutorial, explore the code discussion in this doc set, which has a special focus on how code for running on RDU is different from code in other environments.
The learning map above points to some additional materials — for example, even if you’re trying out the simplest model, you most likely want to go to the API Reference .

The tutorials in this doc set use different code than tutorials included in /opt/sambaflow/apps. Tutorial examples have been updated and streamlined.

Table 1. Tutorial code, README, and code discussion
Tutorial	Description	Code and README	Code discussion
Hello SambaFlow (logreg)	Learn how to compile and and run training. The tutorial code downloads the dataset.	hello_world on Github	Examine logreg model code
Intermediate (lenet)	Step through a complete machine learning workflow. Includes data preparation, compile and training run, and running inference.	lenet on Github	Examine LeNet model code
Conversion 101	Understand model conversion by looking at a simple CNN model. Includes two solutions: One uses an integrated loss function, another uses an external loss function.	Basics in Convert a simple model to SambaFlow	Model functions and changes Model with an external loss function
Transformers on RDU	Use a pretrained Hugging Face GPT-2 model on RDU. The tutorial discusses data preparation, compile and training run, and running inference. The code is in two separate files discusses how inference runs differ from training runs.	Generative NLP on Github	Code elements of the training program Code elements of the inference program

Concepts

Many of us learn best by understanding the big picture first — having a look at a map before exploring unknown territory. The doc set includes several pages that help you get oriented (or dig deep after initial exploration with the code).

Architecture and workflows. Explains how the SambaFlow components fits into the SambaNova hardware and software stack and includes links to resources.
SambaFlow compiler overview. Discusses the compiler stack and explains how model compilation works and includes links to reference materials and other resources.
White paper. SambaNova Accelerated Computing with a Reconfigurable Dataflow Architecture . Discusses the architecture in some detail. Not required reading, but might be of interest.

Reference

All developers have to rely on reference documentation to get their job done. For SambaFlow, we include the following:

Data preparation, SambaNova Runtime, and SambaTune

The following resources in this doc set or elsewhere might help you learn more:

Data preparation scripts. We have a public GitHub repository with two scripts for pretraining data creation, pipeline.py and data_prep.py.
SambaNova Runtime documentation. Information on logs, fault management, and other lower-level procedures.
SambaTune documentation. SambaNova tool for performance optimization (advanced).